To answer the first question, we follow the practice in FCOS [1]}, which introduces multi-level prediction with FPN [2]}. With the use of multi-level feature maps defined as \(\left\lbrace  C_3,C_4,C_5, C_6,C_7 \right\rbrace \) , proposals and ground-truth boxes with different scale would be allocated to the specific feature level. The specific feature level is only used to assign proposals with the specific scale. During experiments, the size range is (0, 64] for \(C_3\) , (64, 128] for \(C_4\) , (128, 256] for \(C_5\) , (256, 512] for \(C_6\)  and (512, \(+\infty \) ] for \(C_7\) . Since proposals with different sizes are allocated to different feature levels, the proposals and ground-truth boxes on the same level would be closer with larger overlap, which further enhances our algorithm.
